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Author Bio

Doug Adamson, BS

Douglas Adamson joined Health Catalyst in June 2012 as Vice President of Architecture. Prior to joining Health Catalyst, Doug worked for GE Healthcare in a number of roles including Chief Technologist, Chief Architect and General Manager of Engineering. Doug also spent 14 years working as a software engineer on the Human Genome Project. He holds a Bachelor of Science degree in Computer Science from Purdue University in West Lafayette, Indiana with additional graduate work in Computer Science and Math.

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Doug Adamson, BS

Big Data in Healthcare Made Simple: Where It Stands Today and Where It’s Going

Health system leaders have questions about big data: When will I need it? How should I prepare? What’s the best way to use it? It’s important to separate the hype of big data from the reality. Where big data stands in healthcare today is a far cry from where it will be in the future. Right now, the best use cases are in academic- or research-focused healthcare institutions. Most healthcare organizations are still tackling issues with their transactional databases and learning how to use those databases effectively. But soon—once the issues of expertise and security have been addressed—big data will play a huge role in care management, predictive analytics, prescriptive analytics, and genomics for everyday patients. The transition to big data will be easier if health systems adopt a late-binding approach to the data now.

Doug Adamson, BS

Data Warehouse Tools: Faster Time-to-Value for Your Healthcare Data Warehouse

When creating a healthcare data warehouse, typically time-to-value will take one to two years. But using our data warehouse tools, we’ve reduced that time to months. Usually a lot of manual labor goes into extracting data from EHRs or other sources systems. Metadata mapping helps by indicated where data is located in each system. However, that mapping process is also typically time-consuming and onerous. Using Health Catalyst’s Source Mart Designer, the mapping is automated and ETL scripts become a cinch. Then we use our Atlas tool to make search for specific data easier and more intuitive.